A synthesis of dryland restoration techniques.

Purpose

To quantitatively examine the efficacy of vegetation restoration in drylands globally.

Questions

1. What is the global extent of research that directly examined restoration of drylands?
   
2. What were the common measures?
   
3. Is the restoration of vegetation a common and primary focus?
   
4. How frequently does the restoration measure outcomes beyond the focal species?
   
5. What were the primary restoration goals as reported by primary authors?
   
6. How much variation was there in the techniques tested and how long were experiments monitored and tested?
   
7. How relatively effective were the techniques?

Step 1. Search

Study-level viz to document patterns in exclusions primarily and the relatie frequenices, at the study level, of major categories of evidence.

#study data####
library(tidyverse)
studies <- read_csv("data/studies.csv")
studies

## # A tibble: 278 x 18
##       ID title technique data  region exclude rationale observations
##    <dbl> <chr> <chr>     <chr> <chr>  <chr>   <chr>     <chr>       
##  1   152 Shor… seeding,… expe… Africa no      <NA>      <NA>        
##  2   180 Rest… chemical… App.… Africa no      <NA>      <NA>        
##  3   229 Infl… soil see… fiel… Africa no      <NA>      <NA>        
##  4   230 Acti… planting  fiel… Africa no      <NA>      <NA>        
##  5   255 The … grazing … fiel… Africa no      <NA>      <NA>        
##  6   262 Reve… seeding,… eper… Africa no      <NA>      <NA>        
##  7   263 The … phytogen… fiel… Africa no      <NA>      <NA>        
##  8   264 Eval… seeding,… fiel… Africa no      <NA>      <NA>        
##  9   271 Patc… natural … fiel… Africa no      <NA>      <NA>        
## 10     4 Fact… natural … App.… Africa no      <NA>      <NA>        
## # ... with 268 more rows, and 10 more variables: disturbance <chr>,
## #   system <chr>, goal <chr>, intervention <chr>, paradigm <chr>,
## #   grazing <chr>, hypothesis <chr>, soil <chr>, benchmark <chr>,
## #   notes <chr>

#quick look at rationale needed
exclusions <- studies %>%
  filter(exclude == "yes")

#quick look at studies with paradigms
evidence <- studies %>%
  filter(exclude == "no")

#library(skimr)
#skim(evidence)

#study-level viz#####
#exclusions
#ggplot(exclusions, aes(rationale, fill = region)) +
 # geom_bar() +
 # coord_flip() +
 # labs(x = "rational for exclusion", y = "frequency") +
 # scale_fill_brewer(palette = "Paired")

ggplot(evidence, aes(disturbance, fill = paradigm)) +
  geom_bar(na.rm = TRUE) +
  coord_flip() +
  scale_fill_brewer(palette = "Paired") +
  labs(y = "frequency")

#ggplot(evidence, aes(region, fill = paradigm)) +
 # geom_bar(na.rm = TRUE) +
 # coord_flip() +
 # scale_fill_brewer(palette = "Paired") +
 # labs(y = "frequency")

#ggplot(evidence, aes(data, fill = paradigm)) +
 # geom_bar(na.rm = TRUE) +
  #coord_flip() +
  #scale_fill_brewer(palette = "Paired") +
  #labs(y = "frequency")

#ggplot(evidence, aes(system, fill = paradigm)) +
 # geom_bar(na.rm = TRUE) +
 # coord_flip() +
 # scale_fill_brewer(palette = "Paired") +
 # labs(y = "frequency")

#ggplot(evidence, aes(goal, fill = paradigm)) +
  #geom_bar(na.rm = TRUE) +
  #coord_flip() +
  #scale_fill_brewer(palette = "Paired") +
  #labs(x = "outcome", y = "frequency")

#step 1 models####
#paradigm
derived.evidence <- evidence %>%
  group_by(technique, data, region, disturbance, goal, paradigm) %>% summarise(n = n())

#active-passive split
m <- glm(n~paradigm, family = poisson, derived.evidence)
anova(m, test="Chisq")

## Analysis of Deviance Table
## 
## Model: poisson, link: log
## 
## Response: n
## 
## Terms added sequentially (first to last)
## 
## 
##          Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL                       167     9.9147         
## paradigm  1 0.045115       166     9.8696   0.8318

#region
m1 <- glm(n~paradigm*region, family = poisson, derived.evidence)
#m1
#summary(m1)
anova(m1, test="Chisq")

## Analysis of Deviance Table
## 
## Model: poisson, link: log
## 
## Response: n
## 
## Terms added sequentially (first to last)
## 
## 
##                 Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL                              167     9.9147         
## paradigm         1 0.045115       166     9.8696   0.8318
## region           6 0.301367       160     9.5682   0.9995
## paradigm:region  6 0.213627       154     9.3546   0.9998

#outcome
m2 <- glm(n~paradigm*goal, family = poisson, derived.evidence)
#m1
#summary(m1)
anova(m2, test="Chisq")

## Analysis of Deviance Table
## 
## Model: poisson, link: log
## 
## Response: n
## 
## Terms added sequentially (first to last)
## 
## 
##               Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL                            167     9.9147         
## paradigm       1 0.045115       166     9.8696   0.8318
## goal           6 0.240941       160     9.6287   0.9997
## paradigm:goal  4 0.301480       156     9.3272   0.9897

#even split between active and passive evidence by all key categories

Step 2. Sort

A summary of sort process using PRISMA.

library(PRISMAstatement)
prisma(found = 1504,
       found_other = 5,
       no_dupes = 1039, 
       screened = 1039, 
       screen_exclusions = 861, 
       full_text = 178,
       full_text_exclusions = 101, 
       qualitative = 77, 
       quantitative = 66,
       width = 800, height = 800)

Step 3. Synthesize

Check data and calculate necessary measures.

#all data
#data <- read_csv("data/data.csv")
#data <- data %>%
  #mutate(lrr = log(mean.t/mean.c), rii = ((mean.t-mean.c)/(mean.t + mean.c)), var.es = ((sd.t^2/n.t*mean.t^2) + (sd.c^2/n.c*mean.c^2)))

#other effect size estimates
#library(compute.es)
#data <- data %>%
  #mutate(d=mes(mean.t, mean.c, sd.t, sd.c, n.t, n.c, level = 95, #cer = 0.2, dig = 2, , id = ID, data = data))
#check metafor

#data from ag and grazing studies that examined restoration in drylands
paperdata <- read_csv("data/paper_data.csv")
paperdata <- paperdata %>%
  mutate(lrr = log(mean.t/mean.c), rii = ((mean.t-mean.c)/(mean.t + mean.c)), var.es = ((sd.t^2/n.t*mean.t^2) + (sd.c^2/n.c*mean.c^2))) #use only lrr now

Step 4. Summarize

Explore summary level data of all data. Explore aggregation levels that support the most reasonable data structure and minimize non-independence issues.

#evidence map####
require(maps)
world<-map_data("world")
map<-ggplot() + geom_polygon(data=world, fill="gray50", aes(x=long, y=lat, group=group))
#map + geom_point(data=paperdata, aes(x=long, y=lat)) +
  #labs(x = "longitude", y = "latitude") #render a literal map, i.e. evidence map, of where we study the niche in deserts globally

#add in levels and color code points on map####
map + geom_point(data=paperdata, aes(x=long, y=lat, color = paradigm)) + 
  scale_color_brewer(palette = "Paired") +
  labs(x = "longitude", y = "latitude", color = "")

#aggregation####
se <- function(x){
  sd(x)/sqrt(length(x))
}

data.simple <- paperdata %>%
  group_by(study.ID, paradigm, technique, measure.success) %>%
  summarise(n = n(), mean.lrr = mean(lrr), mean.rii = mean(rii), mean.var = mean(var.es))

main.data <- paperdata %>%
  group_by(study.ID, paradigm, intervention, outcome) %>%
  summarise(n = n(), mean.lrr = mean(lrr), mean.rii = mean(rii), mean.var = mean(var.es))


#EDA data####
simple.data <- paperdata %>% group_by(study.ID, paradigm, technique, measure.success) %>% summarise(mean.rii = mean(rii), error = se(rii))
simple.data <- na.omit(simple.data)

parad.data <- paperdata %>% group_by(study.ID, paradigm) %>% summarise(mean.rii = mean(rii), error = se(rii))
parad.data <- na.omit(parad.data)

tech.data <- paperdata %>% group_by(study.ID, technique) %>% summarise(mean.rii = mean(rii), error = se(rii))
tech.data <- na.omit(tech.data)

tech.data

## # A tibble: 31 x 4
## # Groups:   study.ID [31]
##    study.ID technique                             mean.rii  error
##       <dbl> <chr>                                    <dbl>  <dbl>
##  1       13 planting                              -0.00830 0.0423
##  2       51 planting, grazing exclusion            0.128   0.0982
##  3       66 seeding, shrub facilitation            0.0762  0.0275
##  4       68 planting                              -0.177   0.0548
##  5       69 grazing exclusion                      0.502   0.0749
##  6       77 mowing, grazing                        0.0613  0.472 
##  7       87 fertilization, biostimulants, seeding -0.0138  0.0137
##  8       88 seeding                               -0.130   0.0153
##  9       95 seeding, planting                     -0.228   0.0415
## 10      104 mycorrhizal                            0.307   0.0230
## # ... with 21 more rows

success.data <- paperdata %>% group_by(study.ID, measure.success) %>% summarise(mean.rii = mean(rii), error = se(rii))
success.data <- na.omit(success.data)


#active
active <- paperdata %>%
  filter(paradigm == "active")

#viz for aggregation####
disturbance.data <- paperdata %>% 
  group_by(study.ID,disturbance, paradigm) %>% 
  count()

disturbance.data2 <- disturbance.data %>% 
  group_by(disturbance,paradigm) %>% 
  count()

ggplot(na.omit(disturbance.data2), aes(disturbance,nn, fill=paradigm))+ 
  geom_bar(stat = "identity") + 
  coord_flip(ylim=0:44) + 
  scale_fill_brewer(palette = "Blues")

intervention.data <- active %>% 
  group_by(study.ID,intervention, paradigm) %>% 
  count()

intervention.data2 <- intervention.data %>% 
  group_by(intervention,paradigm) %>% 
  count()

#ggplot(na.omit(intervention.data2), aes(intervention,nn, fill=paradigm)) +
  #geom_bar(stat = "identity") + 
  #coord_flip(ylim=0:44) + 
  #scale_fill_brewer(palette = "Blues")

technique.data <- paperdata %>% 
  group_by(study.ID,technique, paradigm) %>% 
  count()

technique.data2 <- technique.data %>% 
  group_by(technique,paradigm) %>% 
  count()

technique.data3 <- technique.data2 %>% 
  group_by(technique) %>% 
  count()


ggplot(na.omit(data.simple), aes(technique, n, fill = paradigm)) + 
  geom_bar(stat = "identity") + 
  coord_flip() + 
  scale_fill_brewer(palette = "Paired")

ggplot(na.omit(data.simple), aes(measure.success, n, fill = paradigm)) + 
  geom_bar(stat = "identity") + 
  coord_flip() + 
  scale_fill_brewer(palette = "Paired")

#better
ggplot(main.data, aes(intervention, n, fill = paradigm)) + 
  geom_bar(stat = "identity") + 
  coord_flip() + 
  scale_fill_brewer(palette = "Paired") +
  labs(fill = "")

ggplot(main.data, aes(outcome, n, fill = paradigm)) + 
  geom_bar(stat = "identity") + 
  coord_flip() + 
  scale_fill_brewer(palette = "Paired") +
  labs(fill = "")

Step 5a. EDA

Exploratory data analyses to understand data and QA/QC using Rii.

Step 5b. Models

Meta and conventional statistical models to explore relative efficacy.

Step 5. Synthesis stats

#p-value meta
#library(metap)
#all data for metas but cleaned for na's
#all data for meta cleaned
mdata.all <- paperdata %>%
  filter(!is.na(lrr)) %>%
  filter(!is.na(var.es)) %>%
  filter(!is.na(n.t)) %>%
  filter(!is.na(p)) %>%
  filter(!is.na(intervention)) %>%
  filter(is.finite(lrr))

#active only data for meta cleaned up
mdata <- paperdata %>%
  filter(paradigm == "active") %>%
  filter(!is.na(lrr)) %>%
  filter(!is.na(var.es)) %>%
  filter(!is.na(n.t)) %>%
  filter(!is.na(p)) %>%
  filter(!is.na(intervention)) %>%
  filter(is.finite(lrr))

#aggregated data for metas var estimated with central tendency
#note - could also bootstrap mean variance here instead of arithematic mean
simple.mdata <- mdata %>%
  group_by(intervention) %>%
  summarise(lrr = mean(lrr), var.es = mean(var.es), n = mean(n.t))

simple.mdata.2 <- mdata %>%
  group_by(intervention, outcome) %>%
  summarise(lrr = mean(lrr), var.es = mean(var.es), n = mean(n.t))  

simple.mdata.var <- mdata %>%
  group_by(intervention) %>%
  summarise(lrr = mean(lrr), var.es = se(var.es), n = mean(n.t))

simple.mdata2.var <- mdata %>%
  group_by(intervention, outcome) %>%
  summarise(lrr = mean(lrr), var.es = se(var.es), n = mean(n.t))

#metas with p-values####
#schweder(mdata$p)
#sumz(p, data = mdata)
#mdata %>%
  #split(.$intervention) %>%
  #purrr::map(~sumz(.$p)) 
#sumlog(mdata$p)

#metas with meta package on effect size measures####
library(meta)
#all data non-aggregated
#m <- metagen(lrr, var.es, studlab = ID, byvar = intervention, data = mdata)
#summary(m)
#funnel(m)
#metabias(m)
#forest(m, sortvar = intervention)

m <- metagen(lrr, var.es, studlab = ID, byvar = intervention, subset = paradigm == "active", data = mdata.all)
summary(m)

## Number of studies combined: k = 665
## 
##                                          95%-CI            z  p-value
## Fixed effect model   -0.0063 [-0.0063; -0.0063] -40590292.09        0
## Random effects model  0.2266 [ 0.1715;  0.2816]         8.07 < 0.0001
## 
## Quantifying heterogeneity:
## tau^2 = 0.1848; H = 21775022.93 [21775022.02; 21775023.84]; I^2 = 100.0% [100.0%; 100.0%]
## 
## Test of heterogeneity:
##                      Q d.f. p-value
##  314836678046932224.00  664       0
## 
## Results for subgroups (fixed effect model):
##                                 k                     95%-CI
## intervention = vegetation     442 -0.0063 [-0.0063; -0.0063]
## intervention = soil           148  0.7206 [ 0.7204;  0.7209]
## intervention = water addition  75  0.0037 [ 0.0025;  0.0049]
##                                                   Q   tau^2    I^2
## intervention = vegetation     314836678003004992.00  0.1848 100.0%
## intervention = soil                      2625290.13  0.0583 100.0%
## intervention = water addition                109.93 <0.0001  32.7%
## 
## Test for subgroup differences (fixed effect model):
##                                    Q d.f. p-value
## Between groups           41301796.69    2       0
## Within groups  314836678005630400.00  662       0
## 
## Results for subgroups (random effects model):
##                                 k                   95%-CI
## intervention = vegetation     442 0.2160 [ 0.1549; 0.2771]
## intervention = soil           148 0.2970 [ 0.2183; 0.3756]
## intervention = water addition  75 0.0073 [-0.0012; 0.0158]
##                                                   Q   tau^2    I^2
## intervention = vegetation     314836678003004992.00  0.1848 100.0%
## intervention = soil                      2625290.13  0.0583 100.0%
## intervention = water addition                109.93 <0.0001  32.7%
## 
## Test for subgroup differences (random effects model):
##                      Q d.f.  p-value
## Between groups   94.11    2 < 0.0001
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2

m <- metagen(lrr, var.es, studlab = ID, byvar = intervention, subset = paradigm == "passive", data = mdata.all)
summary(m)

## Number of studies combined: k = 330
## 
##                                          95%-CI           z  p-value
## Fixed effect model   -0.0463 [-0.0463; -0.0463] -1181815.78        0
## Random effects model -0.3632 [-0.4637; -0.2628]       -7.09 < 0.0001
## 
## Quantifying heterogeneity:
## tau^2 = 0.4167; H = 864963.56 [864962.52; 864964.60]; I^2 = 100.0% [100.0%; 100.0%]
## 
## Test of heterogeneity:
##                   Q d.f. p-value
##  246145284902920.38  329       0
## 
## Results for subgroups (fixed effect model):
##                                    k                     95%-CI
## intervention = vegetation        108  0.4187 [ 0.4187;  0.4187]
## intervention = grazing exclusion  25  0.3958 [ 0.3958;  0.3959]
## intervention = soil              197 -0.6888 [-0.6888; -0.6888]
##                                                  Q  tau^2    I^2
## intervention = vegetation        28560273455660.51 0.0968 100.0%
## intervention = grazing exclusion       16640578.62 0.0096 100.0%
## intervention = soil              23314502087392.00 0.0951 100.0%
## 
## Test for subgroup differences (fixed effect model):
##                                 Q d.f. p-value
## Between groups 194270492719289.25    2       0
## Within groups   51874792183631.12  327       0
## 
## Results for subgroups (random effects model):
##                                    k                     95%-CI
## intervention = vegetation        108  0.1542 [ 0.0294;  0.2789]
## intervention = grazing exclusion  25  0.2869 [ 0.2260;  0.3478]
## intervention = soil              197 -0.4834 [-0.5410; -0.4259]
##                                                  Q  tau^2    I^2
## intervention = vegetation        28560273455660.51 0.0968 100.0%
## intervention = grazing exclusion       16640578.62 0.0096 100.0%
## intervention = soil              23314502087392.00 0.0951 100.0%
## 
## Test for subgroup differences (random effects model):
##                       Q d.f.  p-value
## Between groups   341.41    2 < 0.0001
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2

m <- metagen(lrr, var.es, studlab = ID, byvar = paradigm, data = mdata.all)
summary(m)

## Number of studies combined: k = 995
## 
##                                          95%-CI            z p-value
## Fixed effect model   -0.0063 [-0.0063; -0.0063] -40594636.11       0
## Random effects model  0.0036 [-0.0393;  0.0465]         0.17  0.8686
## 
## Quantifying heterogeneity:
## tau^2 = 0.1848; H = 17804094.53 [17804093.80; 17804095.26]; I^2 = 100.0% [100.0%; 100.0%]
## 
## Test of heterogeneity:
##                      Q d.f. p-value
##  315083867351259776.00  994       0
## 
## Results for subgroups (fixed effect model):
##                      k                     95%-CI                     Q
## paradigm = active  665 -0.0063 [-0.0063; -0.0063] 314836678046932224.00
## paradigm = passive 330 -0.0463 [-0.0463; -0.0463]    246145284902920.38
##                     tau^2    I^2
## paradigm = active  0.1848 100.0%
## paradigm = passive 0.4167 100.0%
## 
## Test for subgroup differences (fixed effect model):
##                                    Q d.f. p-value
## Between groups      1044019424645.23    1       0
## Within groups  315082823331835136.00  993       0
## 
## Results for subgroups (random effects model):
##                      k                     95%-CI                     Q
## paradigm = active  665  0.2266 [ 0.1715;  0.2816] 314836678046932224.00
## paradigm = passive 330 -0.3632 [-0.4637; -0.2628]    246145284902920.38
##                     tau^2    I^2
## paradigm = active  0.1848 100.0%
## paradigm = passive 0.4167 100.0%
## 
## Test for subgroup differences (random effects model):
##                       Q d.f.  p-value
## Between groups   101.85    1 < 0.0001
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2

#m.study <- metagen(lrr, var.es, studlab = study.ID, byvar = intervention, data = mdata)
#summary(m.study)
#funnel(m)
#metabias(m)
#forest(m, sortvar = intervention)

#aggregated data
#m1 <- metagen(lrr, var.es, studlab = intervention, data = simple.mdata)
#summary(m1)
#funnel(m1)
#metabias(m1)
#forest(m, sortvar = intervention)

#different var estimate
#m2 <- metagen(lrr, var.es, studlab = intervention, data = simple.mdata.var)
#summary(m2)
#funnel(m2)
#metabias(m2)
#forest(m, sortvar = intervention)


#m3 <- metagen(lrr, var.es, studlab = intervention, byvar = outcome, data = simple.mdata.2)
#summary(m3)
#funnel(m)
#radial(m3)
#metabias(m2)
#forest(m, sortvar = intervention)

#m4 <- metagen(lrr, var.es, studlab = intervention, byvar = outcome, data = simple.mdata2.var)
#summary(m4)
#funnel(m)
#radial(m4)
#metabias(m2)
#forest(m, sortvar = intervention)

#forest(m1, sortvar = intervention, fontsize = 10, xlim = c(-2, 2))

#forest(m2, sortvar = intervention, fontsize = 10, xlim = c(-2, 2))

#forest(m3, sortvar = intervention, fontsize = 10, xlim = c(-2, 2))

#forest(m4, sortvar = intervention, fontsize = 10, xlim = c(-2, 2))

#t-tests for lrr diff from 0
#mdata %>%
  #split(.$intervention) %>%
  #purrr::map(~sumz(.$lrr)) 

#effect sizes plots
#need better ci estimates
#ggplot(simple.mdata, aes(intervention, lrr)) +
 # ylim(c(-2,2)) +
#  geom_point(position = position_dodge(width = 0.5)) + 
 # labs(x = "", y = "lrr") +
 # coord_flip() +
 # geom_errorbar(aes(ymin=lrr-var.es, ymax=lrr+var.es), width=.05, position = position_dodge(width = 0.5)) +
 # geom_hline(yintercept = 0, colour="grey", linetype = "longdash")

#ggplot(simple.mdata.2, aes(intervention, lrr, color = outcome)) +
 # ylim(c(-2,2)) +
  #geom_point(position = position_dodge(width = 0.5)) + 
 # labs(x = "", y = "lrr", color = "") +
 # coord_flip() +
  #geom_errorbar(aes(ymin=lrr-var.es, ymax=lrr+var.es), width=.05, position = #position_dodge(width = 0.5)) +
  #geom_hline(yintercept = 0, colour="grey", linetype = "longdash")

#ggplot(mdata, aes(lrr, color = intervention)) +
  #geom_freqpoly(binwidth = 0.5, size = 2) + 
  #xlim(c(-10, 10)) +
  #labs(x = "lrr", y = "frequency", color = "") +
  #geom_vline(xintercept = 0, colour="grey", linetype = "longdash") +
  #scale_color_brewer()

#ggplot(mdata, aes(lrr, fill = intervention)) +
  #geom_dotplot(binwidth = 1) + 
 # xlim(c(-10, 10)) +
 # labs(x = "lrr", y = "frequency", fill = "") +
 # geom_vline(xintercept = 0, colour="grey", linetype = "longdash") +
 # scale_fill_brewer()